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Ethics is a topic of conversation everywhere in the AI community. Many organizations are flaunting the ethical standards that they’ve created or revamped for their organizations, trying to show that they are on the right side of history. But while it’s clear to most people that the machines should follow ethical rules, I don’t think we’ve done a good job of explaining the limitations of implementing those rules and why we still need to develop an ethical framework for machines. After all, don’t we already have ethical frameworks to use? Yes, we do, but for the behavior of people in society, not machines automating our world. A productive conversation about regulating AI will depend on us figuring out how we even translate our stated values, whatever they may be, into a language that machines can understand.

The point of creating this map was to emphasize that the strength lies in the Canadian AI Ecosystem, as opposed to just one city’s. This year, we’ve seen ties strengthen, but also some weaknesses exposed. What I see now is a corridor of cities, each remarkable in their own right, coming together into a cohesive form that is comparable in size and influence with the US East Coast or Paris and London.

I reached out for help a little while ago on Twitter and on LinkedIn to assess the size and state of the global AI talent pool—a crucial issue for the entire industry going forward. Thank you to those of you from around the world who responded in large numbers. Your generous input has gone into a new report that we at Element AI have developed. We now have a more detailed picture of the size and characteristics of the pool of AI experts going into 2018. I see this report as a living document that will continue growing with others’ contributions. Our broadest measure of the global talent pool is 22,000 individuals: it remains clear that the fight for talent will continue into the foreseeable future. If you can help add more to this detailing of the global talent pool, you can reach me with the contact form or on Twitter.

You can see the full report here at jfgagne.ai/talent.

Below are some of my observations on what I see happening around the world.

I often get asked what are the most important skills for a student to learn going into the coming decade of new AI technology. I have some ideas about why I’m asked this, but it still surprises me how desperate some people are to know the “secret” winning skills of the future.

The World Economic Forum’s own list for 2020 is basically a shuffle of their list from 2015, with complex problem solving at the top of both. And while I don’t know exactly how long it’s been around, I don’t think it’s a particularly new idea that college education is about developing critical thinking skills, learning how to learn, and being able to determine cause and effect in a complex system.